Synthetic POMDPs to Challenge Memory-Augmented RL: Memory Demand Structure Modeling

2025-08-06

AI Summary

Existing POMDP benchmarks lack controllable mechanisms for regulating memory demand, hindering systematic evaluation of memory-augmented reinforcement learning (MARL) algorithms. Method: We propose the Memory Demand Structure (MDS) — a theoretical framework enabling interpretable modeling and explicit control of memory complexity in POMDPs. Leveraging MDS, we design a theory-driven environment synthesis method that integrates linear dynamical systems, state aggregation, and reward redistribution to generate families of POMDPs with predefined, graded memory difficulty levels. Contribution/Results: We construct a benchmark suite with strictly increasing memory difficulty. Empirical evaluation demonstrates its effectiveness in discriminating among distinct memory architectures—including RNNs, Transformers, and external memory modules—thereby supporting principled algorithm selection. Moreover, our framework advances fundamental understanding of how memory mechanisms operate within MARL, offering both diagnostic capability and theoretical insight into memory–task alignment.

Technology Category

• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Multiagent Systems: Mechanism Design
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recent research has developed benchmarks for memory-augmented reinforcement learning (RL) algorithms, providing Partially Observable Markov Decision Process (POMDP) environments where agents depend on past observations to make decisions. While many benchmarks incorporate sufficiently complex real-world problems, they lack controllability over the degree of challenges posed to memory models. In contrast, synthetic environments enable fine-grained manipulation of dynamics, making them critical for detailed and rigorous evaluation of memory-augmented RL. Our study focuses on POMDP synthesis with three key contributions: 1. A theoretical framework for analyzing POMDPs, grounded in Memory Demand Structure (MDS), transition invariance, and related concepts; 2. A methodology leveraging linear process dynamics, state aggregation, and reward redistribution to construct customized POMDPs with predefined properties; 3. Empirically validated series of POMDP environments with increasing difficulty levels, designed based on our theoretical insights. Our work clarifies the challenges of memory-augmented RL in solving POMDPs, provides guidelines for analyzing and designing POMDP environments, and offers empirical support for selecting memory models in RL tasks.

Problem

• Lack of controllable memory challenges in RL benchmarks
• Need for synthetic POMDPs with customizable properties
• Evaluating memory-augmented RL models with varying difficulty levels

Innovation

• Synthetic POMDPs with controlled memory challenges
• Linear dynamics and state aggregation methodology
• Empirically validated difficulty levels in POMDPs